As a Senior AI Engineer, youll focus on building and scaling AI-driven solutions that power personalised experiences for millions of ASOS customers. Youll design and implement robust AI systems leveraging LLMs, generative AI, and advanced data pipelines, ensuring ethical, secure, and high-performing solutions. Youll collaborate closely with Applied Scientists, ML Engineers, and Product teams to accelerate delivery and improve the quality of AI systems by providing reusable templates, prompt engineering strategies, and infrastructure that supports experimentation. What youll be doing • Designing and developing AI solutions using LLMs and generative AI, optimising prompts and workflows for high-quality outputs. • Building scalable data pipelines and applying advanced data preparation techniques to ensure integrity and quality. • Driving standardisation and reusability of AI workflows, enabling consistency across diverse product domains. • Implementing MLOps principles for AI systems including CI/CD, model registries, monitoring, and feature management. • Partnering with Cloud Infrastructure teams to design secure, cost-efficient environments for AI workloads. • Embedding data ethics principles into AI development, proactively mitigating bias and ensuring safe, responsible AI practices. • Fine-tuning pre-trained models for domain-specific tasks to improve performance and relevance. • Designing and implementing robust evaluation frameworks for AI models, including automated testing and benchmarking. About You • Professional experience as an AI Engineer or similar role, with strong exposure to LLMs and generative AI. • Solid understanding of the end-to-end AI lifecycle, from experimentation through deployment and monitoring. • Proficiency in Python and familiarity with ML/AI libraries (e.g., PyTorch, TensorFlow, Hugging Face). • Experience with prompt engineering and optimisation for generative AI systems. • Comfortable working with cloud platforms (preferably Azure) and containerisation (Docker/Kubernetes). • Knowledge of MLOps tools and practices such as MLflow, CI/CD pipelines, and model monitoring. • Passionate about ethical AI, bias mitigation, and improving developer experience through automation and tooling. • Experience in fine-tuning large language models and adapting them for specific use cases. • Strong understanding of evaluation methodologies for AI systems, including quantitative and qualitative metrics. • Ability to design automated evaluation pipelines for continuous monitoring and improvement of AI models.